Load scripts: loads libraries and useful scripts used in the analyses; all .R files contained in scripts at the root of the factory are automatically loaded
Load data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)These scripts will load:
.R files inside /scripts/.R files inside /src/These scripts also contain routines to access the latest clean encrypted data (see next section).
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 401,809 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… miss… e380000… nhs_glo… 1 gl34fe South West
## [90m 2[39m 111 2020-03-18 fema… miss… e380001… nhs_sou… 1 ne325nn North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_air… 8 bd57jr North Eas…
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ash… 7 tn254ab South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 9 n111np London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 11 s752py North Eas…
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 19 ss143hg East of E…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 6 dn227xf North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bat… 9 ba25rp South West
## [90m# … with 401,799 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 65
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 100
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 14
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 7
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 1
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 8
## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 4
## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 4
## 113 2020-06-21 East of England 3
## 114 2020-06-22 East of England 6
## 115 2020-06-23 East of England 5
## 116 2020-06-24 East of England 4
## 117 2020-06-25 East of England 1
## 118 2020-06-26 East of England 5
## 119 2020-06-27 East of England 6
## 120 2020-06-28 East of England 8
## 121 2020-06-29 East of England 4
## 122 2020-06-30 East of England 5
## 123 2020-07-01 East of England 2
## 124 2020-07-02 East of England 5
## 125 2020-07-03 East of England 0
## 126 2020-07-04 East of England 3
## 127 2020-07-05 East of England 1
## 128 2020-07-06 East of England 2
## 129 2020-07-07 East of England 2
## 130 2020-07-08 East of England 0
## 131 2020-07-09 East of England 8
## 132 2020-07-10 East of England 4
## 133 2020-07-11 East of England 2
## 134 2020-07-12 East of England 1
## 135 2020-07-13 East of England 8
## 136 2020-07-14 East of England 2
## 137 2020-07-15 East of England 0
## 138 2020-07-16 East of England 0
## 139 2020-07-17 East of England 0
## 140 2020-07-18 East of England 0
## 141 2020-07-19 East of England 1
## 142 2020-07-20 East of England 1
## 143 2020-07-21 East of England 1
## 144 2020-07-22 East of England 2
## 145 2020-07-23 East of England 1
## 146 2020-07-24 East of England 1
## 147 2020-07-25 East of England 0
## 148 2020-07-26 East of England 1
## 149 2020-07-27 East of England 1
## 150 2020-07-28 East of England 2
## 151 2020-07-29 East of England 0
## 152 2020-07-30 East of England 0
## 153 2020-07-31 East of England 1
## 154 2020-08-01 East of England 0
## 155 2020-08-02 East of England 0
## 156 2020-08-03 East of England 0
## 157 2020-08-04 East of England 1
## 158 2020-08-05 East of England 1
## 159 2020-08-06 East of England 0
## 160 2020-08-07 East of England 1
## 161 2020-08-08 East of England 0
## 162 2020-08-09 East of England 0
## 163 2020-08-10 East of England 1
## 164 2020-08-11 East of England 2
## 165 2020-08-12 East of England 1
## 166 2020-08-13 East of England 0
## 167 2020-08-14 East of England 1
## 168 2020-08-15 East of England 1
## 169 2020-08-16 East of England 0
## 170 2020-08-17 East of England 0
## 171 2020-08-18 East of England 2
## 172 2020-08-19 East of England 1
## 173 2020-08-20 East of England 1
## 174 2020-08-21 East of England 0
## 175 2020-08-22 East of England 1
## 176 2020-08-23 East of England 1
## 177 2020-08-24 East of England 0
## 178 2020-08-25 East of England 0
## 179 2020-08-26 East of England 1
## 180 2020-08-27 East of England 1
## 181 2020-08-28 East of England 0
## 182 2020-08-29 East of England 0
## 183 2020-08-30 East of England 0
## 184 2020-08-31 East of England 0
## 185 2020-09-01 East of England 0
## 186 2020-09-02 East of England 0
## 187 2020-09-03 East of England 1
## 188 2020-09-04 East of England 1
## 189 2020-09-05 East of England 0
## 190 2020-09-06 East of England 1
## 191 2020-09-07 East of England 0
## 192 2020-09-08 East of England 0
## 193 2020-09-09 East of England 0
## 194 2020-09-10 East of England 0
## 195 2020-09-11 East of England 0
## 196 2020-09-12 East of England 0
## 197 2020-09-13 East of England 1
## 198 2020-09-14 East of England 1
## 199 2020-09-15 East of England 0
## 200 2020-09-16 East of England 0
## 201 2020-09-17 East of England 0
## 202 2020-09-18 East of England 0
## 203 2020-09-19 East of England 0
## 204 2020-09-20 East of England 2
## 205 2020-09-21 East of England 0
## 206 2020-09-22 East of England 2
## 207 2020-09-23 East of England 1
## 208 2020-09-24 East of England 0
## 209 2020-09-25 East of England 1
## 210 2020-09-26 East of England 1
## 211 2020-09-27 East of England 1
## 212 2020-09-28 East of England 2
## 213 2020-09-29 East of England 2
## 214 2020-09-30 East of England 2
## 215 2020-10-01 East of England 2
## 216 2020-10-02 East of England 1
## 217 2020-10-03 East of England 1
## 218 2020-10-04 East of England 0
## 219 2020-10-05 East of England 0
## 220 2020-10-06 East of England 4
## 221 2020-10-07 East of England 6
## 222 2020-10-08 East of England 3
## 223 2020-10-09 East of England 1
## 224 2020-10-10 East of England 6
## 225 2020-10-11 East of England 2
## 226 2020-10-12 East of England 2
## 227 2020-10-13 East of England 1
## 228 2020-10-14 East of England 3
## 229 2020-10-15 East of England 4
## 230 2020-10-16 East of England 5
## 231 2020-10-17 East of England 6
## 232 2020-10-18 East of England 7
## 233 2020-10-19 East of England 5
## 234 2020-10-20 East of England 9
## 235 2020-10-21 East of England 7
## 236 2020-10-22 East of England 7
## 237 2020-10-23 East of England 14
## 238 2020-10-24 East of England 1
## 239 2020-10-25 East of England 10
## 240 2020-10-26 East of England 10
## 241 2020-10-27 East of England 8
## 242 2020-10-28 East of England 12
## 243 2020-10-29 East of England 10
## 244 2020-10-30 East of England 12
## 245 2020-10-31 East of England 15
## 246 2020-11-01 East of England 14
## 247 2020-11-02 East of England 9
## 248 2020-11-03 East of England 14
## 249 2020-11-04 East of England 11
## 250 2020-11-05 East of England 11
## 251 2020-11-06 East of England 18
## 252 2020-11-07 East of England 10
## 253 2020-11-08 East of England 13
## 254 2020-11-09 East of England 16
## 255 2020-11-10 East of England 26
## 256 2020-11-11 East of England 14
## 257 2020-11-12 East of England 14
## 258 2020-11-13 East of England 21
## 259 2020-11-14 East of England 19
## 260 2020-11-15 East of England 13
## 261 2020-11-16 East of England 11
## 262 2020-11-17 East of England 17
## 263 2020-11-18 East of England 19
## 264 2020-11-19 East of England 23
## 265 2020-11-20 East of England 24
## 266 2020-11-21 East of England 19
## 267 2020-11-22 East of England 21
## 268 2020-11-23 East of England 18
## 269 2020-11-24 East of England 21
## 270 2020-11-25 East of England 19
## 271 2020-11-26 East of England 19
## 272 2020-11-27 East of England 14
## 273 2020-11-28 East of England 28
## 274 2020-11-29 East of England 19
## 275 2020-11-30 East of England 22
## 276 2020-12-01 East of England 24
## 277 2020-12-02 East of England 18
## 278 2020-12-03 East of England 23
## 279 2020-12-04 East of England 24
## 280 2020-12-05 East of England 25
## 281 2020-12-06 East of England 19
## 282 2020-12-07 East of England 16
## 283 2020-12-08 East of England 26
## 284 2020-12-09 East of England 19
## 285 2020-12-10 East of England 32
## 286 2020-12-11 East of England 32
## 287 2020-12-12 East of England 26
## 288 2020-12-13 East of England 23
## 289 2020-12-14 East of England 29
## 290 2020-12-15 East of England 35
## 291 2020-12-16 East of England 29
## 292 2020-12-17 East of England 41
## 293 2020-12-18 East of England 42
## 294 2020-12-19 East of England 53
## 295 2020-12-20 East of England 50
## 296 2020-12-21 East of England 62
## 297 2020-12-22 East of England 51
## 298 2020-12-23 East of England 60
## 299 2020-12-24 East of England 51
## 300 2020-12-25 East of England 55
## 301 2020-12-26 East of England 57
## 302 2020-12-27 East of England 47
## 303 2020-12-28 East of England 63
## 304 2020-12-29 East of England 46
## 305 2020-12-30 East of England 67
## 306 2020-12-31 East of England 76
## 307 2021-01-01 East of England 76
## 308 2021-01-02 East of England 55
## 309 2021-01-03 East of England 69
## 310 2021-01-04 East of England 73
## 311 2021-01-05 East of England 92
## 312 2021-01-06 East of England 83
## 313 2021-01-07 East of England 87
## 314 2021-01-08 East of England 66
## 315 2021-01-09 East of England 95
## 316 2021-01-10 East of England 82
## 317 2021-01-11 East of England 55
## 318 2021-01-12 East of England 17
## 319 2020-03-01 London 0
## 320 2020-03-02 London 0
## 321 2020-03-03 London 0
## 322 2020-03-04 London 0
## 323 2020-03-05 London 0
## 324 2020-03-06 London 1
## 325 2020-03-07 London 0
## 326 2020-03-08 London 0
## 327 2020-03-09 London 1
## 328 2020-03-10 London 0
## 329 2020-03-11 London 5
## 330 2020-03-12 London 6
## 331 2020-03-13 London 10
## 332 2020-03-14 London 13
## 333 2020-03-15 London 9
## 334 2020-03-16 London 15
## 335 2020-03-17 London 23
## 336 2020-03-18 London 28
## 337 2020-03-19 London 25
## 338 2020-03-20 London 44
## 339 2020-03-21 London 49
## 340 2020-03-22 London 54
## 341 2020-03-23 London 63
## 342 2020-03-24 London 86
## 343 2020-03-25 London 112
## 344 2020-03-26 London 130
## 345 2020-03-27 London 130
## 346 2020-03-28 London 123
## 347 2020-03-29 London 145
## 348 2020-03-30 London 151
## 349 2020-03-31 London 183
## 350 2020-04-01 London 202
## 351 2020-04-02 London 191
## 352 2020-04-03 London 199
## 353 2020-04-04 London 231
## 354 2020-04-05 London 195
## 355 2020-04-06 London 198
## 356 2020-04-07 London 220
## 357 2020-04-08 London 239
## 358 2020-04-09 London 207
## 359 2020-04-10 London 171
## 360 2020-04-11 London 178
## 361 2020-04-12 London 159
## 362 2020-04-13 London 166
## 363 2020-04-14 London 143
## 364 2020-04-15 London 143
## 365 2020-04-16 London 140
## 366 2020-04-17 London 101
## 367 2020-04-18 London 101
## 368 2020-04-19 London 103
## 369 2020-04-20 London 96
## 370 2020-04-21 London 96
## 371 2020-04-22 London 109
## 372 2020-04-23 London 77
## 373 2020-04-24 London 71
## 374 2020-04-25 London 58
## 375 2020-04-26 London 53
## 376 2020-04-27 London 52
## 377 2020-04-28 London 44
## 378 2020-04-29 London 45
## 379 2020-04-30 London 40
## 380 2020-05-01 London 41
## 381 2020-05-02 London 41
## 382 2020-05-03 London 36
## 383 2020-05-04 London 30
## 384 2020-05-05 London 25
## 385 2020-05-06 London 37
## 386 2020-05-07 London 37
## 387 2020-05-08 London 31
## 388 2020-05-09 London 23
## 389 2020-05-10 London 26
## 390 2020-05-11 London 18
## 391 2020-05-12 London 18
## 392 2020-05-13 London 17
## 393 2020-05-14 London 20
## 394 2020-05-15 London 19
## 395 2020-05-16 London 14
## 396 2020-05-17 London 15
## 397 2020-05-18 London 11
## 398 2020-05-19 London 14
## 399 2020-05-20 London 19
## 400 2020-05-21 London 12
## 401 2020-05-22 London 10
## 402 2020-05-23 London 6
## 403 2020-05-24 London 7
## 404 2020-05-25 London 9
## 405 2020-05-26 London 14
## 406 2020-05-27 London 7
## 407 2020-05-28 London 8
## 408 2020-05-29 London 7
## 409 2020-05-30 London 12
## 410 2020-05-31 London 6
## 411 2020-06-01 London 10
## 412 2020-06-02 London 8
## 413 2020-06-03 London 6
## 414 2020-06-04 London 8
## 415 2020-06-05 London 4
## 416 2020-06-06 London 0
## 417 2020-06-07 London 5
## 418 2020-06-08 London 5
## 419 2020-06-09 London 5
## 420 2020-06-10 London 8
## 421 2020-06-11 London 5
## 422 2020-06-12 London 3
## 423 2020-06-13 London 3
## 424 2020-06-14 London 3
## 425 2020-06-15 London 1
## 426 2020-06-16 London 2
## 427 2020-06-17 London 1
## 428 2020-06-18 London 2
## 429 2020-06-19 London 5
## 430 2020-06-20 London 3
## 431 2020-06-21 London 4
## 432 2020-06-22 London 2
## 433 2020-06-23 London 1
## 434 2020-06-24 London 4
## 435 2020-06-25 London 3
## 436 2020-06-26 London 2
## 437 2020-06-27 London 1
## 438 2020-06-28 London 2
## 439 2020-06-29 London 2
## 440 2020-06-30 London 1
## 441 2020-07-01 London 3
## 442 2020-07-02 London 2
## 443 2020-07-03 London 2
## 444 2020-07-04 London 1
## 445 2020-07-05 London 3
## 446 2020-07-06 London 2
## 447 2020-07-07 London 1
## 448 2020-07-08 London 3
## 449 2020-07-09 London 4
## 450 2020-07-10 London 0
## 451 2020-07-11 London 1
## 452 2020-07-12 London 1
## 453 2020-07-13 London 1
## 454 2020-07-14 London 0
## 455 2020-07-15 London 2
## 456 2020-07-16 London 0
## 457 2020-07-17 London 0
## 458 2020-07-18 London 2
## 459 2020-07-19 London 0
## 460 2020-07-20 London 0
## 461 2020-07-21 London 1
## 462 2020-07-22 London 0
## 463 2020-07-23 London 2
## 464 2020-07-24 London 0
## 465 2020-07-25 London 1
## 466 2020-07-26 London 0
## 467 2020-07-27 London 1
## 468 2020-07-28 London 0
## 469 2020-07-29 London 0
## 470 2020-07-30 London 1
## 471 2020-07-31 London 0
## 472 2020-08-01 London 0
## 473 2020-08-02 London 3
## 474 2020-08-03 London 0
## 475 2020-08-04 London 0
## 476 2020-08-05 London 0
## 477 2020-08-06 London 1
## 478 2020-08-07 London 0
## 479 2020-08-08 London 0
## 480 2020-08-09 London 0
## 481 2020-08-10 London 0
## 482 2020-08-11 London 1
## 483 2020-08-12 London 0
## 484 2020-08-13 London 2
## 485 2020-08-14 London 0
## 486 2020-08-15 London 0
## 487 2020-08-16 London 0
## 488 2020-08-17 London 1
## 489 2020-08-18 London 1
## 490 2020-08-19 London 0
## 491 2020-08-20 London 1
## 492 2020-08-21 London 0
## 493 2020-08-22 London 0
## 494 2020-08-23 London 0
## 495 2020-08-24 London 1
## 496 2020-08-25 London 1
## 497 2020-08-26 London 0
## 498 2020-08-27 London 0
## 499 2020-08-28 London 0
## 500 2020-08-29 London 0
## 501 2020-08-30 London 0
## 502 2020-08-31 London 1
## 503 2020-09-01 London 0
## 504 2020-09-02 London 1
## 505 2020-09-03 London 1
## 506 2020-09-04 London 0
## 507 2020-09-05 London 0
## 508 2020-09-06 London 2
## 509 2020-09-07 London 0
## 510 2020-09-08 London 0
## 511 2020-09-09 London 0
## 512 2020-09-10 London 2
## 513 2020-09-11 London 1
## 514 2020-09-12 London 1
## 515 2020-09-13 London 0
## 516 2020-09-14 London 0
## 517 2020-09-15 London 1
## 518 2020-09-16 London 2
## 519 2020-09-17 London 2
## 520 2020-09-18 London 1
## 521 2020-09-19 London 3
## 522 2020-09-20 London 3
## 523 2020-09-21 London 2
## 524 2020-09-22 London 6
## 525 2020-09-23 London 4
## 526 2020-09-24 London 3
## 527 2020-09-25 London 1
## 528 2020-09-26 London 1
## 529 2020-09-27 London 1
## 530 2020-09-28 London 3
## 531 2020-09-29 London 7
## 532 2020-09-30 London 6
## 533 2020-10-01 London 4
## 534 2020-10-02 London 1
## 535 2020-10-03 London 3
## 536 2020-10-04 London 2
## 537 2020-10-05 London 7
## 538 2020-10-06 London 4
## 539 2020-10-07 London 6
## 540 2020-10-08 London 6
## 541 2020-10-09 London 7
## 542 2020-10-10 London 3
## 543 2020-10-11 London 5
## 544 2020-10-12 London 7
## 545 2020-10-13 London 4
## 546 2020-10-14 London 6
## 547 2020-10-15 London 13
## 548 2020-10-16 London 6
## 549 2020-10-17 London 2
## 550 2020-10-18 London 5
## 551 2020-10-19 London 11
## 552 2020-10-20 London 8
## 553 2020-10-21 London 14
## 554 2020-10-22 London 12
## 555 2020-10-23 London 7
## 556 2020-10-24 London 18
## 557 2020-10-25 London 10
## 558 2020-10-26 London 10
## 559 2020-10-27 London 12
## 560 2020-10-28 London 23
## 561 2020-10-29 London 14
## 562 2020-10-30 London 17
## 563 2020-10-31 London 7
## 564 2020-11-01 London 17
## 565 2020-11-02 London 16
## 566 2020-11-03 London 10
## 567 2020-11-04 London 18
## 568 2020-11-05 London 17
## 569 2020-11-06 London 12
## 570 2020-11-07 London 21
## 571 2020-11-08 London 15
## 572 2020-11-09 London 28
## 573 2020-11-10 London 14
## 574 2020-11-11 London 15
## 575 2020-11-12 London 16
## 576 2020-11-13 London 14
## 577 2020-11-14 London 21
## 578 2020-11-15 London 18
## 579 2020-11-16 London 29
## 580 2020-11-17 London 29
## 581 2020-11-18 London 23
## 582 2020-11-19 London 24
## 583 2020-11-20 London 20
## 584 2020-11-21 London 18
## 585 2020-11-22 London 29
## 586 2020-11-23 London 19
## 587 2020-11-24 London 26
## 588 2020-11-25 London 30
## 589 2020-11-26 London 25
## 590 2020-11-27 London 28
## 591 2020-11-28 London 23
## 592 2020-11-29 London 40
## 593 2020-11-30 London 19
## 594 2020-12-01 London 28
## 595 2020-12-02 London 30
## 596 2020-12-03 London 27
## 597 2020-12-04 London 29
## 598 2020-12-05 London 24
## 599 2020-12-06 London 24
## 600 2020-12-07 London 29
## 601 2020-12-08 London 35
## 602 2020-12-09 London 27
## 603 2020-12-10 London 29
## 604 2020-12-11 London 26
## 605 2020-12-12 London 33
## 606 2020-12-13 London 33
## 607 2020-12-14 London 37
## 608 2020-12-15 London 49
## 609 2020-12-16 London 35
## 610 2020-12-17 London 56
## 611 2020-12-18 London 40
## 612 2020-12-19 London 39
## 613 2020-12-20 London 52
## 614 2020-12-21 London 56
## 615 2020-12-22 London 56
## 616 2020-12-23 London 57
## 617 2020-12-24 London 63
## 618 2020-12-25 London 80
## 619 2020-12-26 London 82
## 620 2020-12-27 London 90
## 621 2020-12-28 London 88
## 622 2020-12-29 London 107
## 623 2020-12-30 London 98
## 624 2020-12-31 London 107
## 625 2021-01-01 London 110
## 626 2021-01-02 London 122
## 627 2021-01-03 London 106
## 628 2021-01-04 London 145
## 629 2021-01-05 London 139
## 630 2021-01-06 London 141
## 631 2021-01-07 London 150
## 632 2021-01-08 London 123
## 633 2021-01-09 London 115
## 634 2021-01-10 London 110
## 635 2021-01-11 London 87
## 636 2021-01-12 London 14
## 637 2020-03-01 Midlands 0
## 638 2020-03-02 Midlands 0
## 639 2020-03-03 Midlands 1
## 640 2020-03-04 Midlands 0
## 641 2020-03-05 Midlands 0
## 642 2020-03-06 Midlands 0
## 643 2020-03-07 Midlands 0
## 644 2020-03-08 Midlands 2
## 645 2020-03-09 Midlands 1
## 646 2020-03-10 Midlands 0
## 647 2020-03-11 Midlands 2
## 648 2020-03-12 Midlands 6
## 649 2020-03-13 Midlands 5
## 650 2020-03-14 Midlands 4
## 651 2020-03-15 Midlands 5
## 652 2020-03-16 Midlands 11
## 653 2020-03-17 Midlands 8
## 654 2020-03-18 Midlands 13
## 655 2020-03-19 Midlands 8
## 656 2020-03-20 Midlands 28
## 657 2020-03-21 Midlands 13
## 658 2020-03-22 Midlands 31
## 659 2020-03-23 Midlands 33
## 660 2020-03-24 Midlands 41
## 661 2020-03-25 Midlands 48
## 662 2020-03-26 Midlands 64
## 663 2020-03-27 Midlands 72
## 664 2020-03-28 Midlands 89
## 665 2020-03-29 Midlands 92
## 666 2020-03-30 Midlands 90
## 667 2020-03-31 Midlands 123
## 668 2020-04-01 Midlands 140
## 669 2020-04-02 Midlands 142
## 670 2020-04-03 Midlands 124
## 671 2020-04-04 Midlands 151
## 672 2020-04-05 Midlands 164
## 673 2020-04-06 Midlands 140
## 674 2020-04-07 Midlands 123
## 675 2020-04-08 Midlands 186
## 676 2020-04-09 Midlands 140
## 677 2020-04-10 Midlands 127
## 678 2020-04-11 Midlands 142
## 679 2020-04-12 Midlands 139
## 680 2020-04-13 Midlands 120
## 681 2020-04-14 Midlands 116
## 682 2020-04-15 Midlands 147
## 683 2020-04-16 Midlands 102
## 684 2020-04-17 Midlands 118
## 685 2020-04-18 Midlands 115
## 686 2020-04-19 Midlands 93
## 687 2020-04-20 Midlands 107
## 688 2020-04-21 Midlands 86
## 689 2020-04-22 Midlands 78
## 690 2020-04-23 Midlands 103
## 691 2020-04-24 Midlands 79
## 692 2020-04-25 Midlands 72
## 693 2020-04-26 Midlands 81
## 694 2020-04-27 Midlands 74
## 695 2020-04-28 Midlands 68
## 696 2020-04-29 Midlands 53
## 697 2020-04-30 Midlands 56
## 698 2020-05-01 Midlands 64
## 699 2020-05-02 Midlands 51
## 700 2020-05-03 Midlands 52
## 701 2020-05-04 Midlands 61
## 702 2020-05-05 Midlands 59
## 703 2020-05-06 Midlands 59
## 704 2020-05-07 Midlands 48
## 705 2020-05-08 Midlands 34
## 706 2020-05-09 Midlands 37
## 707 2020-05-10 Midlands 42
## 708 2020-05-11 Midlands 33
## 709 2020-05-12 Midlands 45
## 710 2020-05-13 Midlands 40
## 711 2020-05-14 Midlands 39
## 712 2020-05-15 Midlands 40
## 713 2020-05-16 Midlands 34
## 714 2020-05-17 Midlands 31
## 715 2020-05-18 Midlands 36
## 716 2020-05-19 Midlands 35
## 717 2020-05-20 Midlands 36
## 718 2020-05-21 Midlands 32
## 719 2020-05-22 Midlands 27
## 720 2020-05-23 Midlands 34
## 721 2020-05-24 Midlands 20
## 722 2020-05-25 Midlands 26
## 723 2020-05-26 Midlands 33
## 724 2020-05-27 Midlands 29
## 725 2020-05-28 Midlands 28
## 726 2020-05-29 Midlands 20
## 727 2020-05-30 Midlands 21
## 728 2020-05-31 Midlands 22
## 729 2020-06-01 Midlands 20
## 730 2020-06-02 Midlands 22
## 731 2020-06-03 Midlands 24
## 732 2020-06-04 Midlands 16
## 733 2020-06-05 Midlands 21
## 734 2020-06-06 Midlands 20
## 735 2020-06-07 Midlands 17
## 736 2020-06-08 Midlands 16
## 737 2020-06-09 Midlands 18
## 738 2020-06-10 Midlands 15
## 739 2020-06-11 Midlands 13
## 740 2020-06-12 Midlands 12
## 741 2020-06-13 Midlands 6
## 742 2020-06-14 Midlands 18
## 743 2020-06-15 Midlands 12
## 744 2020-06-16 Midlands 15
## 745 2020-06-17 Midlands 11
## 746 2020-06-18 Midlands 15
## 747 2020-06-19 Midlands 10
## 748 2020-06-20 Midlands 15
## 749 2020-06-21 Midlands 14
## 750 2020-06-22 Midlands 14
## 751 2020-06-23 Midlands 16
## 752 2020-06-24 Midlands 15
## 753 2020-06-25 Midlands 18
## 754 2020-06-26 Midlands 5
## 755 2020-06-27 Midlands 5
## 756 2020-06-28 Midlands 7
## 757 2020-06-29 Midlands 6
## 758 2020-06-30 Midlands 6
## 759 2020-07-01 Midlands 7
## 760 2020-07-02 Midlands 10
## 761 2020-07-03 Midlands 3
## 762 2020-07-04 Midlands 4
## 763 2020-07-05 Midlands 6
## 764 2020-07-06 Midlands 5
## 765 2020-07-07 Midlands 3
## 766 2020-07-08 Midlands 5
## 767 2020-07-09 Midlands 9
## 768 2020-07-10 Midlands 3
## 769 2020-07-11 Midlands 0
## 770 2020-07-12 Midlands 5
## 771 2020-07-13 Midlands 1
## 772 2020-07-14 Midlands 1
## 773 2020-07-15 Midlands 6
## 774 2020-07-16 Midlands 2
## 775 2020-07-17 Midlands 3
## 776 2020-07-18 Midlands 3
## 777 2020-07-19 Midlands 3
## 778 2020-07-20 Midlands 3
## 779 2020-07-21 Midlands 1
## 780 2020-07-22 Midlands 2
## 781 2020-07-23 Midlands 6
## 782 2020-07-24 Midlands 1
## 783 2020-07-25 Midlands 4
## 784 2020-07-26 Midlands 4
## 785 2020-07-27 Midlands 5
## 786 2020-07-28 Midlands 1
## 787 2020-07-29 Midlands 1
## 788 2020-07-30 Midlands 1
## 789 2020-07-31 Midlands 2
## 790 2020-08-01 Midlands 0
## 791 2020-08-02 Midlands 1
## 792 2020-08-03 Midlands 2
## 793 2020-08-04 Midlands 1
## 794 2020-08-05 Midlands 1
## 795 2020-08-06 Midlands 0
## 796 2020-08-07 Midlands 3
## 797 2020-08-08 Midlands 2
## 798 2020-08-09 Midlands 0
## 799 2020-08-10 Midlands 0
## 800 2020-08-11 Midlands 2
## 801 2020-08-12 Midlands 0
## 802 2020-08-13 Midlands 0
## 803 2020-08-14 Midlands 0
## 804 2020-08-15 Midlands 1
## 805 2020-08-16 Midlands 0
## 806 2020-08-17 Midlands 0
## 807 2020-08-18 Midlands 0
## 808 2020-08-19 Midlands 0
## 809 2020-08-20 Midlands 0
## 810 2020-08-21 Midlands 1
## 811 2020-08-22 Midlands 0
## 812 2020-08-23 Midlands 0
## 813 2020-08-24 Midlands 0
## 814 2020-08-25 Midlands 2
## 815 2020-08-26 Midlands 3
## 816 2020-08-27 Midlands 2
## 817 2020-08-28 Midlands 1
## 818 2020-08-29 Midlands 0
## 819 2020-08-30 Midlands 2
## 820 2020-08-31 Midlands 1
## 821 2020-09-01 Midlands 0
## 822 2020-09-02 Midlands 2
## 823 2020-09-03 Midlands 0
## 824 2020-09-04 Midlands 0
## 825 2020-09-05 Midlands 0
## 826 2020-09-06 Midlands 1
## 827 2020-09-07 Midlands 1
## 828 2020-09-08 Midlands 3
## 829 2020-09-09 Midlands 0
## 830 2020-09-10 Midlands 1
## 831 2020-09-11 Midlands 1
## 832 2020-09-12 Midlands 2
## 833 2020-09-13 Midlands 4
## 834 2020-09-14 Midlands 1
## 835 2020-09-15 Midlands 2
## 836 2020-09-16 Midlands 3
## 837 2020-09-17 Midlands 2
## 838 2020-09-18 Midlands 5
## 839 2020-09-19 Midlands 2
## 840 2020-09-20 Midlands 7
## 841 2020-09-21 Midlands 3
## 842 2020-09-22 Midlands 4
## 843 2020-09-23 Midlands 10
## 844 2020-09-24 Midlands 7
## 845 2020-09-25 Midlands 4
## 846 2020-09-26 Midlands 5
## 847 2020-09-27 Midlands 9
## 848 2020-09-28 Midlands 6
## 849 2020-09-29 Midlands 4
## 850 2020-09-30 Midlands 5
## 851 2020-10-01 Midlands 8
## 852 2020-10-02 Midlands 7
## 853 2020-10-03 Midlands 6
## 854 2020-10-04 Midlands 7
## 855 2020-10-05 Midlands 6
## 856 2020-10-06 Midlands 5
## 857 2020-10-07 Midlands 9
## 858 2020-10-08 Midlands 8
## 859 2020-10-09 Midlands 7
## 860 2020-10-10 Midlands 2
## 861 2020-10-11 Midlands 15
## 862 2020-10-12 Midlands 7
## 863 2020-10-13 Midlands 16
## 864 2020-10-14 Midlands 12
## 865 2020-10-15 Midlands 11
## 866 2020-10-16 Midlands 18
## 867 2020-10-17 Midlands 25
## 868 2020-10-18 Midlands 11
## 869 2020-10-19 Midlands 14
## 870 2020-10-20 Midlands 19
## 871 2020-10-21 Midlands 15
## 872 2020-10-22 Midlands 34
## 873 2020-10-23 Midlands 32
## 874 2020-10-24 Midlands 24
## 875 2020-10-25 Midlands 30
## 876 2020-10-26 Midlands 33
## 877 2020-10-27 Midlands 38
## 878 2020-10-28 Midlands 30
## 879 2020-10-29 Midlands 42
## 880 2020-10-30 Midlands 42
## 881 2020-10-31 Midlands 50
## 882 2020-11-01 Midlands 44
## 883 2020-11-02 Midlands 58
## 884 2020-11-03 Midlands 37
## 885 2020-11-04 Midlands 67
## 886 2020-11-05 Midlands 50
## 887 2020-11-06 Midlands 43
## 888 2020-11-07 Midlands 60
## 889 2020-11-08 Midlands 55
## 890 2020-11-09 Midlands 67
## 891 2020-11-10 Midlands 68
## 892 2020-11-11 Midlands 56
## 893 2020-11-12 Midlands 64
## 894 2020-11-13 Midlands 47
## 895 2020-11-14 Midlands 66
## 896 2020-11-15 Midlands 72
## 897 2020-11-16 Midlands 66
## 898 2020-11-17 Midlands 66
## 899 2020-11-18 Midlands 83
## 900 2020-11-19 Midlands 72
## 901 2020-11-20 Midlands 87
## 902 2020-11-21 Midlands 59
## 903 2020-11-22 Midlands 84
## 904 2020-11-23 Midlands 80
## 905 2020-11-24 Midlands 73
## 906 2020-11-25 Midlands 74
## 907 2020-11-26 Midlands 77
## 908 2020-11-27 Midlands 78
## 909 2020-11-28 Midlands 80
## 910 2020-11-29 Midlands 86
## 911 2020-11-30 Midlands 79
## 912 2020-12-01 Midlands 74
## 913 2020-12-02 Midlands 64
## 914 2020-12-03 Midlands 82
## 915 2020-12-04 Midlands 66
## 916 2020-12-05 Midlands 71
## 917 2020-12-06 Midlands 75
## 918 2020-12-07 Midlands 68
## 919 2020-12-08 Midlands 64
## 920 2020-12-09 Midlands 61
## 921 2020-12-10 Midlands 73
## 922 2020-12-11 Midlands 65
## 923 2020-12-12 Midlands 79
## 924 2020-12-13 Midlands 77
## 925 2020-12-14 Midlands 76
## 926 2020-12-15 Midlands 71
## 927 2020-12-16 Midlands 71
## 928 2020-12-17 Midlands 83
## 929 2020-12-18 Midlands 78
## 930 2020-12-19 Midlands 56
## 931 2020-12-20 Midlands 66
## 932 2020-12-21 Midlands 84
## 933 2020-12-22 Midlands 70
## 934 2020-12-23 Midlands 57
## 935 2020-12-24 Midlands 66
## 936 2020-12-25 Midlands 78
## 937 2020-12-26 Midlands 71
## 938 2020-12-27 Midlands 84
## 939 2020-12-28 Midlands 62
## 940 2020-12-29 Midlands 80
## 941 2020-12-30 Midlands 96
## 942 2020-12-31 Midlands 86
## 943 2021-01-01 Midlands 74
## 944 2021-01-02 Midlands 72
## 945 2021-01-03 Midlands 69
## 946 2021-01-04 Midlands 91
## 947 2021-01-05 Midlands 100
## 948 2021-01-06 Midlands 109
## 949 2021-01-07 Midlands 95
## 950 2021-01-08 Midlands 112
## 951 2021-01-09 Midlands 123
## 952 2021-01-10 Midlands 109
## 953 2021-01-11 Midlands 106
## 954 2021-01-12 Midlands 17
## 955 2020-03-01 North East and Yorkshire 0
## 956 2020-03-02 North East and Yorkshire 0
## 957 2020-03-03 North East and Yorkshire 0
## 958 2020-03-04 North East and Yorkshire 0
## 959 2020-03-05 North East and Yorkshire 0
## 960 2020-03-06 North East and Yorkshire 0
## 961 2020-03-07 North East and Yorkshire 0
## 962 2020-03-08 North East and Yorkshire 0
## 963 2020-03-09 North East and Yorkshire 0
## 964 2020-03-10 North East and Yorkshire 0
## 965 2020-03-11 North East and Yorkshire 0
## 966 2020-03-12 North East and Yorkshire 0
## 967 2020-03-13 North East and Yorkshire 0
## 968 2020-03-14 North East and Yorkshire 0
## 969 2020-03-15 North East and Yorkshire 2
## 970 2020-03-16 North East and Yorkshire 3
## 971 2020-03-17 North East and Yorkshire 1
## 972 2020-03-18 North East and Yorkshire 2
## 973 2020-03-19 North East and Yorkshire 6
## 974 2020-03-20 North East and Yorkshire 5
## 975 2020-03-21 North East and Yorkshire 6
## 976 2020-03-22 North East and Yorkshire 7
## 977 2020-03-23 North East and Yorkshire 9
## 978 2020-03-24 North East and Yorkshire 8
## 979 2020-03-25 North East and Yorkshire 18
## 980 2020-03-26 North East and Yorkshire 21
## 981 2020-03-27 North East and Yorkshire 28
## 982 2020-03-28 North East and Yorkshire 35
## 983 2020-03-29 North East and Yorkshire 38
## 984 2020-03-30 North East and Yorkshire 64
## 985 2020-03-31 North East and Yorkshire 60
## 986 2020-04-01 North East and Yorkshire 67
## 987 2020-04-02 North East and Yorkshire 75
## 988 2020-04-03 North East and Yorkshire 100
## 989 2020-04-04 North East and Yorkshire 105
## 990 2020-04-05 North East and Yorkshire 92
## 991 2020-04-06 North East and Yorkshire 96
## 992 2020-04-07 North East and Yorkshire 102
## 993 2020-04-08 North East and Yorkshire 107
## 994 2020-04-09 North East and Yorkshire 111
## 995 2020-04-10 North East and Yorkshire 117
## 996 2020-04-11 North East and Yorkshire 98
## 997 2020-04-12 North East and Yorkshire 84
## 998 2020-04-13 North East and Yorkshire 94
## 999 2020-04-14 North East and Yorkshire 107
## 1000 2020-04-15 North East and Yorkshire 96
## 1001 2020-04-16 North East and Yorkshire 103
## 1002 2020-04-17 North East and Yorkshire 88
## 1003 2020-04-18 North East and Yorkshire 95
## 1004 2020-04-19 North East and Yorkshire 88
## 1005 2020-04-20 North East and Yorkshire 100
## 1006 2020-04-21 North East and Yorkshire 76
## 1007 2020-04-22 North East and Yorkshire 84
## 1008 2020-04-23 North East and Yorkshire 63
## 1009 2020-04-24 North East and Yorkshire 72
## 1010 2020-04-25 North East and Yorkshire 69
## 1011 2020-04-26 North East and Yorkshire 65
## 1012 2020-04-27 North East and Yorkshire 65
## 1013 2020-04-28 North East and Yorkshire 57
## 1014 2020-04-29 North East and Yorkshire 69
## 1015 2020-04-30 North East and Yorkshire 57
## 1016 2020-05-01 North East and Yorkshire 64
## 1017 2020-05-02 North East and Yorkshire 48
## 1018 2020-05-03 North East and Yorkshire 40
## 1019 2020-05-04 North East and Yorkshire 49
## 1020 2020-05-05 North East and Yorkshire 40
## 1021 2020-05-06 North East and Yorkshire 51
## 1022 2020-05-07 North East and Yorkshire 45
## 1023 2020-05-08 North East and Yorkshire 42
## 1024 2020-05-09 North East and Yorkshire 44
## 1025 2020-05-10 North East and Yorkshire 40
## 1026 2020-05-11 North East and Yorkshire 29
## 1027 2020-05-12 North East and Yorkshire 27
## 1028 2020-05-13 North East and Yorkshire 28
## 1029 2020-05-14 North East and Yorkshire 31
## 1030 2020-05-15 North East and Yorkshire 32
## 1031 2020-05-16 North East and Yorkshire 35
## 1032 2020-05-17 North East and Yorkshire 26
## 1033 2020-05-18 North East and Yorkshire 30
## 1034 2020-05-19 North East and Yorkshire 27
## 1035 2020-05-20 North East and Yorkshire 22
## 1036 2020-05-21 North East and Yorkshire 33
## 1037 2020-05-22 North East and Yorkshire 22
## 1038 2020-05-23 North East and Yorkshire 18
## 1039 2020-05-24 North East and Yorkshire 26
## 1040 2020-05-25 North East and Yorkshire 21
## 1041 2020-05-26 North East and Yorkshire 21
## 1042 2020-05-27 North East and Yorkshire 22
## 1043 2020-05-28 North East and Yorkshire 21
## 1044 2020-05-29 North East and Yorkshire 25
## 1045 2020-05-30 North East and Yorkshire 20
## 1046 2020-05-31 North East and Yorkshire 20
## 1047 2020-06-01 North East and Yorkshire 17
## 1048 2020-06-02 North East and Yorkshire 23
## 1049 2020-06-03 North East and Yorkshire 24
## 1050 2020-06-04 North East and Yorkshire 17
## 1051 2020-06-05 North East and Yorkshire 18
## 1052 2020-06-06 North East and Yorkshire 21
## 1053 2020-06-07 North East and Yorkshire 14
## 1054 2020-06-08 North East and Yorkshire 11
## 1055 2020-06-09 North East and Yorkshire 12
## 1056 2020-06-10 North East and Yorkshire 19
## 1057 2020-06-11 North East and Yorkshire 7
## 1058 2020-06-12 North East and Yorkshire 9
## 1059 2020-06-13 North East and Yorkshire 10
## 1060 2020-06-14 North East and Yorkshire 11
## 1061 2020-06-15 North East and Yorkshire 9
## 1062 2020-06-16 North East and Yorkshire 10
## 1063 2020-06-17 North East and Yorkshire 9
## 1064 2020-06-18 North East and Yorkshire 11
## 1065 2020-06-19 North East and Yorkshire 6
## 1066 2020-06-20 North East and Yorkshire 5
## 1067 2020-06-21 North East and Yorkshire 4
## 1068 2020-06-22 North East and Yorkshire 7
## 1069 2020-06-23 North East and Yorkshire 8
## 1070 2020-06-24 North East and Yorkshire 10
## 1071 2020-06-25 North East and Yorkshire 4
## 1072 2020-06-26 North East and Yorkshire 8
## 1073 2020-06-27 North East and Yorkshire 4
## 1074 2020-06-28 North East and Yorkshire 5
## 1075 2020-06-29 North East and Yorkshire 2
## 1076 2020-06-30 North East and Yorkshire 7
## 1077 2020-07-01 North East and Yorkshire 1
## 1078 2020-07-02 North East and Yorkshire 5
## 1079 2020-07-03 North East and Yorkshire 4
## 1080 2020-07-04 North East and Yorkshire 4
## 1081 2020-07-05 North East and Yorkshire 3
## 1082 2020-07-06 North East and Yorkshire 2
## 1083 2020-07-07 North East and Yorkshire 3
## 1084 2020-07-08 North East and Yorkshire 3
## 1085 2020-07-09 North East and Yorkshire 0
## 1086 2020-07-10 North East and Yorkshire 3
## 1087 2020-07-11 North East and Yorkshire 1
## 1088 2020-07-12 North East and Yorkshire 4
## 1089 2020-07-13 North East and Yorkshire 1
## 1090 2020-07-14 North East and Yorkshire 1
## 1091 2020-07-15 North East and Yorkshire 2
## 1092 2020-07-16 North East and Yorkshire 3
## 1093 2020-07-17 North East and Yorkshire 1
## 1094 2020-07-18 North East and Yorkshire 2
## 1095 2020-07-19 North East and Yorkshire 2
## 1096 2020-07-20 North East and Yorkshire 1
## 1097 2020-07-21 North East and Yorkshire 1
## 1098 2020-07-22 North East and Yorkshire 6
## 1099 2020-07-23 North East and Yorkshire 0
## 1100 2020-07-24 North East and Yorkshire 1
## 1101 2020-07-25 North East and Yorkshire 5
## 1102 2020-07-26 North East and Yorkshire 1
## 1103 2020-07-27 North East and Yorkshire 0
## 1104 2020-07-28 North East and Yorkshire 2
## 1105 2020-07-29 North East and Yorkshire 1
## 1106 2020-07-30 North East and Yorkshire 0
## 1107 2020-07-31 North East and Yorkshire 1
## 1108 2020-08-01 North East and Yorkshire 3
## 1109 2020-08-02 North East and Yorkshire 2
## 1110 2020-08-03 North East and Yorkshire 1
## 1111 2020-08-04 North East and Yorkshire 3
## 1112 2020-08-05 North East and Yorkshire 1
## 1113 2020-08-06 North East and Yorkshire 4
## 1114 2020-08-07 North East and Yorkshire 0
## 1115 2020-08-08 North East and Yorkshire 2
## 1116 2020-08-09 North East and Yorkshire 3
## 1117 2020-08-10 North East and Yorkshire 3
## 1118 2020-08-11 North East and Yorkshire 2
## 1119 2020-08-12 North East and Yorkshire 2
## 1120 2020-08-13 North East and Yorkshire 0
## 1121 2020-08-14 North East and Yorkshire 1
## 1122 2020-08-15 North East and Yorkshire 1
## 1123 2020-08-16 North East and Yorkshire 0
## 1124 2020-08-17 North East and Yorkshire 6
## 1125 2020-08-18 North East and Yorkshire 1
## 1126 2020-08-19 North East and Yorkshire 0
## 1127 2020-08-20 North East and Yorkshire 0
## 1128 2020-08-21 North East and Yorkshire 1
## 1129 2020-08-22 North East and Yorkshire 1
## 1130 2020-08-23 North East and Yorkshire 3
## 1131 2020-08-24 North East and Yorkshire 0
## 1132 2020-08-25 North East and Yorkshire 2
## 1133 2020-08-26 North East and Yorkshire 2
## 1134 2020-08-27 North East and Yorkshire 1
## 1135 2020-08-28 North East and Yorkshire 0
## 1136 2020-08-29 North East and Yorkshire 1
## 1137 2020-08-30 North East and Yorkshire 0
## 1138 2020-08-31 North East and Yorkshire 0
## 1139 2020-09-01 North East and Yorkshire 2
## 1140 2020-09-02 North East and Yorkshire 3
## 1141 2020-09-03 North East and Yorkshire 1
## 1142 2020-09-04 North East and Yorkshire 1
## 1143 2020-09-05 North East and Yorkshire 2
## 1144 2020-09-06 North East and Yorkshire 1
## 1145 2020-09-07 North East and Yorkshire 0
## 1146 2020-09-08 North East and Yorkshire 1
## 1147 2020-09-09 North East and Yorkshire 2
## 1148 2020-09-10 North East and Yorkshire 0
## 1149 2020-09-11 North East and Yorkshire 3
## 1150 2020-09-12 North East and Yorkshire 1
## 1151 2020-09-13 North East and Yorkshire 3
## 1152 2020-09-14 North East and Yorkshire 4
## 1153 2020-09-15 North East and Yorkshire 3
## 1154 2020-09-16 North East and Yorkshire 3
## 1155 2020-09-17 North East and Yorkshire 5
## 1156 2020-09-18 North East and Yorkshire 6
## 1157 2020-09-19 North East and Yorkshire 2
## 1158 2020-09-20 North East and Yorkshire 9
## 1159 2020-09-21 North East and Yorkshire 7
## 1160 2020-09-22 North East and Yorkshire 5
## 1161 2020-09-23 North East and Yorkshire 6
## 1162 2020-09-24 North East and Yorkshire 3
## 1163 2020-09-25 North East and Yorkshire 5
## 1164 2020-09-26 North East and Yorkshire 7
## 1165 2020-09-27 North East and Yorkshire 10
## 1166 2020-09-28 North East and Yorkshire 6
## 1167 2020-09-29 North East and Yorkshire 7
## 1168 2020-09-30 North East and Yorkshire 7
## 1169 2020-10-01 North East and Yorkshire 8
## 1170 2020-10-02 North East and Yorkshire 16
## 1171 2020-10-03 North East and Yorkshire 12
## 1172 2020-10-04 North East and Yorkshire 13
## 1173 2020-10-05 North East and Yorkshire 10
## 1174 2020-10-06 North East and Yorkshire 15
## 1175 2020-10-07 North East and Yorkshire 13
## 1176 2020-10-08 North East and Yorkshire 16
## 1177 2020-10-09 North East and Yorkshire 10
## 1178 2020-10-10 North East and Yorkshire 16
## 1179 2020-10-11 North East and Yorkshire 16
## 1180 2020-10-12 North East and Yorkshire 15
## 1181 2020-10-13 North East and Yorkshire 21
## 1182 2020-10-14 North East and Yorkshire 20
## 1183 2020-10-15 North East and Yorkshire 23
## 1184 2020-10-16 North East and Yorkshire 24
## 1185 2020-10-17 North East and Yorkshire 34
## 1186 2020-10-18 North East and Yorkshire 22
## 1187 2020-10-19 North East and Yorkshire 34
## 1188 2020-10-20 North East and Yorkshire 36
## 1189 2020-10-21 North East and Yorkshire 42
## 1190 2020-10-22 North East and Yorkshire 33
## 1191 2020-10-23 North East and Yorkshire 31
## 1192 2020-10-24 North East and Yorkshire 34
## 1193 2020-10-25 North East and Yorkshire 35
## 1194 2020-10-26 North East and Yorkshire 45
## 1195 2020-10-27 North East and Yorkshire 45
## 1196 2020-10-28 North East and Yorkshire 39
## 1197 2020-10-29 North East and Yorkshire 51
## 1198 2020-10-30 North East and Yorkshire 48
## 1199 2020-10-31 North East and Yorkshire 58
## 1200 2020-11-01 North East and Yorkshire 48
## 1201 2020-11-02 North East and Yorkshire 50
## 1202 2020-11-03 North East and Yorkshire 48
## 1203 2020-11-04 North East and Yorkshire 57
## 1204 2020-11-05 North East and Yorkshire 57
## 1205 2020-11-06 North East and Yorkshire 57
## 1206 2020-11-07 North East and Yorkshire 75
## 1207 2020-11-08 North East and Yorkshire 61
## 1208 2020-11-09 North East and Yorkshire 87
## 1209 2020-11-10 North East and Yorkshire 65
## 1210 2020-11-11 North East and Yorkshire 59
## 1211 2020-11-12 North East and Yorkshire 77
## 1212 2020-11-13 North East and Yorkshire 78
## 1213 2020-11-14 North East and Yorkshire 72
## 1214 2020-11-15 North East and Yorkshire 77
## 1215 2020-11-16 North East and Yorkshire 51
## 1216 2020-11-17 North East and Yorkshire 68
## 1217 2020-11-18 North East and Yorkshire 79
## 1218 2020-11-19 North East and Yorkshire 72
## 1219 2020-11-20 North East and Yorkshire 75
## 1220 2020-11-21 North East and Yorkshire 54
## 1221 2020-11-22 North East and Yorkshire 80
## 1222 2020-11-23 North East and Yorkshire 83
## 1223 2020-11-24 North East and Yorkshire 81
## 1224 2020-11-25 North East and Yorkshire 70
## 1225 2020-11-26 North East and Yorkshire 64
## 1226 2020-11-27 North East and Yorkshire 62
## 1227 2020-11-28 North East and Yorkshire 76
## 1228 2020-11-29 North East and Yorkshire 61
## 1229 2020-11-30 North East and Yorkshire 55
## 1230 2020-12-01 North East and Yorkshire 44
## 1231 2020-12-02 North East and Yorkshire 59
## 1232 2020-12-03 North East and Yorkshire 71
## 1233 2020-12-04 North East and Yorkshire 64
## 1234 2020-12-05 North East and Yorkshire 48
## 1235 2020-12-06 North East and Yorkshire 63
## 1236 2020-12-07 North East and Yorkshire 49
## 1237 2020-12-08 North East and Yorkshire 54
## 1238 2020-12-09 North East and Yorkshire 49
## 1239 2020-12-10 North East and Yorkshire 55
## 1240 2020-12-11 North East and Yorkshire 55
## 1241 2020-12-12 North East and Yorkshire 55
## 1242 2020-12-13 North East and Yorkshire 51
## 1243 2020-12-14 North East and Yorkshire 49
## 1244 2020-12-15 North East and Yorkshire 54
## 1245 2020-12-16 North East and Yorkshire 39
## 1246 2020-12-17 North East and Yorkshire 49
## 1247 2020-12-18 North East and Yorkshire 58
## 1248 2020-12-19 North East and Yorkshire 48
## 1249 2020-12-20 North East and Yorkshire 52
## 1250 2020-12-21 North East and Yorkshire 35
## 1251 2020-12-22 North East and Yorkshire 57
## 1252 2020-12-23 North East and Yorkshire 56
## 1253 2020-12-24 North East and Yorkshire 48
## 1254 2020-12-25 North East and Yorkshire 56
## 1255 2020-12-26 North East and Yorkshire 65
## 1256 2020-12-27 North East and Yorkshire 67
## 1257 2020-12-28 North East and Yorkshire 61
## 1258 2020-12-29 North East and Yorkshire 57
## 1259 2020-12-30 North East and Yorkshire 41
## 1260 2020-12-31 North East and Yorkshire 47
## 1261 2021-01-01 North East and Yorkshire 68
## 1262 2021-01-02 North East and Yorkshire 55
## 1263 2021-01-03 North East and Yorkshire 48
## 1264 2021-01-04 North East and Yorkshire 61
## 1265 2021-01-05 North East and Yorkshire 61
## 1266 2021-01-06 North East and Yorkshire 56
## 1267 2021-01-07 North East and Yorkshire 65
## 1268 2021-01-08 North East and Yorkshire 65
## 1269 2021-01-09 North East and Yorkshire 46
## 1270 2021-01-10 North East and Yorkshire 74
## 1271 2021-01-11 North East and Yorkshire 57
## 1272 2021-01-12 North East and Yorkshire 15
## 1273 2020-03-01 North West 0
## 1274 2020-03-02 North West 0
## 1275 2020-03-03 North West 0
## 1276 2020-03-04 North West 0
## 1277 2020-03-05 North West 1
## 1278 2020-03-06 North West 0
## 1279 2020-03-07 North West 0
## 1280 2020-03-08 North West 1
## 1281 2020-03-09 North West 0
## 1282 2020-03-10 North West 0
## 1283 2020-03-11 North West 0
## 1284 2020-03-12 North West 2
## 1285 2020-03-13 North West 3
## 1286 2020-03-14 North West 1
## 1287 2020-03-15 North West 4
## 1288 2020-03-16 North West 2
## 1289 2020-03-17 North West 4
## 1290 2020-03-18 North West 6
## 1291 2020-03-19 North West 7
## 1292 2020-03-20 North West 10
## 1293 2020-03-21 North West 11
## 1294 2020-03-22 North West 13
## 1295 2020-03-23 North West 15
## 1296 2020-03-24 North West 21
## 1297 2020-03-25 North West 21
## 1298 2020-03-26 North West 29
## 1299 2020-03-27 North West 36
## 1300 2020-03-28 North West 28
## 1301 2020-03-29 North West 46
## 1302 2020-03-30 North West 67
## 1303 2020-03-31 North West 52
## 1304 2020-04-01 North West 86
## 1305 2020-04-02 North West 96
## 1306 2020-04-03 North West 95
## 1307 2020-04-04 North West 98
## 1308 2020-04-05 North West 102
## 1309 2020-04-06 North West 100
## 1310 2020-04-07 North West 136
## 1311 2020-04-08 North West 127
## 1312 2020-04-09 North West 119
## 1313 2020-04-10 North West 117
## 1314 2020-04-11 North West 138
## 1315 2020-04-12 North West 125
## 1316 2020-04-13 North West 130
## 1317 2020-04-14 North West 130
## 1318 2020-04-15 North West 114
## 1319 2020-04-16 North West 135
## 1320 2020-04-17 North West 98
## 1321 2020-04-18 North West 113
## 1322 2020-04-19 North West 71
## 1323 2020-04-20 North West 83
## 1324 2020-04-21 North West 76
## 1325 2020-04-22 North West 86
## 1326 2020-04-23 North West 85
## 1327 2020-04-24 North West 66
## 1328 2020-04-25 North West 66
## 1329 2020-04-26 North West 55
## 1330 2020-04-27 North West 54
## 1331 2020-04-28 North West 57
## 1332 2020-04-29 North West 63
## 1333 2020-04-30 North West 60
## 1334 2020-05-01 North West 45
## 1335 2020-05-02 North West 56
## 1336 2020-05-03 North West 55
## 1337 2020-05-04 North West 48
## 1338 2020-05-05 North West 48
## 1339 2020-05-06 North West 44
## 1340 2020-05-07 North West 49
## 1341 2020-05-08 North West 42
## 1342 2020-05-09 North West 31
## 1343 2020-05-10 North West 42
## 1344 2020-05-11 North West 35
## 1345 2020-05-12 North West 38
## 1346 2020-05-13 North West 25
## 1347 2020-05-14 North West 26
## 1348 2020-05-15 North West 33
## 1349 2020-05-16 North West 32
## 1350 2020-05-17 North West 24
## 1351 2020-05-18 North West 31
## 1352 2020-05-19 North West 35
## 1353 2020-05-20 North West 27
## 1354 2020-05-21 North West 28
## 1355 2020-05-22 North West 26
## 1356 2020-05-23 North West 31
## 1357 2020-05-24 North West 26
## 1358 2020-05-25 North West 31
## 1359 2020-05-26 North West 27
## 1360 2020-05-27 North West 27
## 1361 2020-05-28 North West 28
## 1362 2020-05-29 North West 20
## 1363 2020-05-30 North West 19
## 1364 2020-05-31 North West 13
## 1365 2020-06-01 North West 12
## 1366 2020-06-02 North West 27
## 1367 2020-06-03 North West 22
## 1368 2020-06-04 North West 22
## 1369 2020-06-05 North West 16
## 1370 2020-06-06 North West 26
## 1371 2020-06-07 North West 20
## 1372 2020-06-08 North West 23
## 1373 2020-06-09 North West 17
## 1374 2020-06-10 North West 16
## 1375 2020-06-11 North West 16
## 1376 2020-06-12 North West 11
## 1377 2020-06-13 North West 10
## 1378 2020-06-14 North West 15
## 1379 2020-06-15 North West 16
## 1380 2020-06-16 North West 16
## 1381 2020-06-17 North West 13
## 1382 2020-06-18 North West 14
## 1383 2020-06-19 North West 7
## 1384 2020-06-20 North West 11
## 1385 2020-06-21 North West 8
## 1386 2020-06-22 North West 11
## 1387 2020-06-23 North West 13
## 1388 2020-06-24 North West 13
## 1389 2020-06-25 North West 15
## 1390 2020-06-26 North West 6
## 1391 2020-06-27 North West 7
## 1392 2020-06-28 North West 9
## 1393 2020-06-29 North West 9
## 1394 2020-06-30 North West 7
## 1395 2020-07-01 North West 3
## 1396 2020-07-02 North West 6
## 1397 2020-07-03 North West 7
## 1398 2020-07-04 North West 4
## 1399 2020-07-05 North West 6
## 1400 2020-07-06 North West 9
## 1401 2020-07-07 North West 8
## 1402 2020-07-08 North West 5
## 1403 2020-07-09 North West 10
## 1404 2020-07-10 North West 2
## 1405 2020-07-11 North West 5
## 1406 2020-07-12 North West 0
## 1407 2020-07-13 North West 6
## 1408 2020-07-14 North West 4
## 1409 2020-07-15 North West 5
## 1410 2020-07-16 North West 2
## 1411 2020-07-17 North West 4
## 1412 2020-07-18 North West 5
## 1413 2020-07-19 North West 3
## 1414 2020-07-20 North West 0
## 1415 2020-07-21 North West 2
## 1416 2020-07-22 North West 3
## 1417 2020-07-23 North West 3
## 1418 2020-07-24 North West 1
## 1419 2020-07-25 North West 1
## 1420 2020-07-26 North West 3
## 1421 2020-07-27 North West 1
## 1422 2020-07-28 North West 1
## 1423 2020-07-29 North West 2
## 1424 2020-07-30 North West 2
## 1425 2020-07-31 North West 0
## 1426 2020-08-01 North West 2
## 1427 2020-08-02 North West 1
## 1428 2020-08-03 North West 8
## 1429 2020-08-04 North West 3
## 1430 2020-08-05 North West 2
## 1431 2020-08-06 North West 2
## 1432 2020-08-07 North West 2
## 1433 2020-08-08 North West 2
## 1434 2020-08-09 North West 3
## 1435 2020-08-10 North West 2
## 1436 2020-08-11 North West 3
## 1437 2020-08-12 North West 0
## 1438 2020-08-13 North West 2
## 1439 2020-08-14 North West 2
## 1440 2020-08-15 North West 6
## 1441 2020-08-16 North West 2
## 1442 2020-08-17 North West 1
## 1443 2020-08-18 North West 2
## 1444 2020-08-19 North West 1
## 1445 2020-08-20 North West 1
## 1446 2020-08-21 North West 4
## 1447 2020-08-22 North West 3
## 1448 2020-08-23 North West 5
## 1449 2020-08-24 North West 4
## 1450 2020-08-25 North West 3
## 1451 2020-08-26 North West 4
## 1452 2020-08-27 North West 1
## 1453 2020-08-28 North West 2
## 1454 2020-08-29 North West 0
## 1455 2020-08-30 North West 2
## 1456 2020-08-31 North West 3
## 1457 2020-09-01 North West 0
## 1458 2020-09-02 North West 2
## 1459 2020-09-03 North West 1
## 1460 2020-09-04 North West 3
## 1461 2020-09-05 North West 6
## 1462 2020-09-06 North West 1
## 1463 2020-09-07 North West 8
## 1464 2020-09-08 North West 6
## 1465 2020-09-09 North West 5
## 1466 2020-09-10 North West 5
## 1467 2020-09-11 North West 1
## 1468 2020-09-12 North West 4
## 1469 2020-09-13 North West 2
## 1470 2020-09-14 North West 4
## 1471 2020-09-15 North West 4
## 1472 2020-09-16 North West 6
## 1473 2020-09-17 North West 7
## 1474 2020-09-18 North West 6
## 1475 2020-09-19 North West 3
## 1476 2020-09-20 North West 2
## 1477 2020-09-21 North West 2
## 1478 2020-09-22 North West 9
## 1479 2020-09-23 North West 14
## 1480 2020-09-24 North West 10
## 1481 2020-09-25 North West 8
## 1482 2020-09-26 North West 14
## 1483 2020-09-27 North West 11
## 1484 2020-09-28 North West 15
## 1485 2020-09-29 North West 12
## 1486 2020-09-30 North West 17
## 1487 2020-10-01 North West 17
## 1488 2020-10-02 North West 20
## 1489 2020-10-03 North West 15
## 1490 2020-10-04 North West 15
## 1491 2020-10-05 North West 15
## 1492 2020-10-06 North West 20
## 1493 2020-10-07 North West 20
## 1494 2020-10-08 North West 22
## 1495 2020-10-09 North West 23
## 1496 2020-10-10 North West 31
## 1497 2020-10-11 North West 31
## 1498 2020-10-12 North West 35
## 1499 2020-10-13 North West 26
## 1500 2020-10-14 North West 35
## 1501 2020-10-15 North West 36
## 1502 2020-10-16 North West 34
## 1503 2020-10-17 North West 52
## 1504 2020-10-18 North West 40
## 1505 2020-10-19 North West 43
## 1506 2020-10-20 North West 48
## 1507 2020-10-21 North West 51
## 1508 2020-10-22 North West 49
## 1509 2020-10-23 North West 50
## 1510 2020-10-24 North West 51
## 1511 2020-10-25 North West 63
## 1512 2020-10-26 North West 53
## 1513 2020-10-27 North West 49
## 1514 2020-10-28 North West 57
## 1515 2020-10-29 North West 74
## 1516 2020-10-30 North West 73
## 1517 2020-10-31 North West 63
## 1518 2020-11-01 North West 76
## 1519 2020-11-02 North West 65
## 1520 2020-11-03 North West 76
## 1521 2020-11-04 North West 64
## 1522 2020-11-05 North West 67
## 1523 2020-11-06 North West 75
## 1524 2020-11-07 North West 79
## 1525 2020-11-08 North West 83
## 1526 2020-11-09 North West 82
## 1527 2020-11-10 North West 68
## 1528 2020-11-11 North West 61
## 1529 2020-11-12 North West 64
## 1530 2020-11-13 North West 81
## 1531 2020-11-14 North West 61
## 1532 2020-11-15 North West 75
## 1533 2020-11-16 North West 74
## 1534 2020-11-17 North West 73
## 1535 2020-11-18 North West 70
## 1536 2020-11-19 North West 67
## 1537 2020-11-20 North West 52
## 1538 2020-11-21 North West 68
## 1539 2020-11-22 North West 52
## 1540 2020-11-23 North West 54
## 1541 2020-11-24 North West 64
## 1542 2020-11-25 North West 65
## 1543 2020-11-26 North West 53
## 1544 2020-11-27 North West 51
## 1545 2020-11-28 North West 46
## 1546 2020-11-29 North West 54
## 1547 2020-11-30 North West 48
## 1548 2020-12-01 North West 53
## 1549 2020-12-02 North West 48
## 1550 2020-12-03 North West 46
## 1551 2020-12-04 North West 46
## 1552 2020-12-05 North West 37
## 1553 2020-12-06 North West 43
## 1554 2020-12-07 North West 50
## 1555 2020-12-08 North West 48
## 1556 2020-12-09 North West 47
## 1557 2020-12-10 North West 49
## 1558 2020-12-11 North West 41
## 1559 2020-12-12 North West 48
## 1560 2020-12-13 North West 40
## 1561 2020-12-14 North West 49
## 1562 2020-12-15 North West 34
## 1563 2020-12-16 North West 40
## 1564 2020-12-17 North West 25
## 1565 2020-12-18 North West 47
## 1566 2020-12-19 North West 45
## 1567 2020-12-20 North West 36
## 1568 2020-12-21 North West 51
## 1569 2020-12-22 North West 52
## 1570 2020-12-23 North West 48
## 1571 2020-12-24 North West 57
## 1572 2020-12-25 North West 52
## 1573 2020-12-26 North West 56
## 1574 2020-12-27 North West 50
## 1575 2020-12-28 North West 48
## 1576 2020-12-29 North West 48
## 1577 2020-12-30 North West 49
## 1578 2020-12-31 North West 60
## 1579 2021-01-01 North West 52
## 1580 2021-01-02 North West 53
## 1581 2021-01-03 North West 55
## 1582 2021-01-04 North West 49
## 1583 2021-01-05 North West 52
## 1584 2021-01-06 North West 63
## 1585 2021-01-07 North West 55
## 1586 2021-01-08 North West 63
## 1587 2021-01-09 North West 53
## 1588 2021-01-10 North West 49
## 1589 2021-01-11 North West 53
## 1590 2021-01-12 North West 17
## 1591 2020-03-01 South East 0
## 1592 2020-03-02 South East 0
## 1593 2020-03-03 South East 1
## 1594 2020-03-04 South East 0
## 1595 2020-03-05 South East 1
## 1596 2020-03-06 South East 0
## 1597 2020-03-07 South East 0
## 1598 2020-03-08 South East 1
## 1599 2020-03-09 South East 1
## 1600 2020-03-10 South East 1
## 1601 2020-03-11 South East 1
## 1602 2020-03-12 South East 0
## 1603 2020-03-13 South East 1
## 1604 2020-03-14 South East 1
## 1605 2020-03-15 South East 5
## 1606 2020-03-16 South East 8
## 1607 2020-03-17 South East 7
## 1608 2020-03-18 South East 10
## 1609 2020-03-19 South East 9
## 1610 2020-03-20 South East 13
## 1611 2020-03-21 South East 7
## 1612 2020-03-22 South East 25
## 1613 2020-03-23 South East 20
## 1614 2020-03-24 South East 22
## 1615 2020-03-25 South East 29
## 1616 2020-03-26 South East 35
## 1617 2020-03-27 South East 36
## 1618 2020-03-28 South East 36
## 1619 2020-03-29 South East 55
## 1620 2020-03-30 South East 58
## 1621 2020-03-31 South East 65
## 1622 2020-04-01 South East 66
## 1623 2020-04-02 South East 55
## 1624 2020-04-03 South East 72
## 1625 2020-04-04 South East 80
## 1626 2020-04-05 South East 82
## 1627 2020-04-06 South East 88
## 1628 2020-04-07 South East 100
## 1629 2020-04-08 South East 83
## 1630 2020-04-09 South East 104
## 1631 2020-04-10 South East 88
## 1632 2020-04-11 South East 88
## 1633 2020-04-12 South East 88
## 1634 2020-04-13 South East 84
## 1635 2020-04-14 South East 65
## 1636 2020-04-15 South East 72
## 1637 2020-04-16 South East 56
## 1638 2020-04-17 South East 86
## 1639 2020-04-18 South East 57
## 1640 2020-04-19 South East 70
## 1641 2020-04-20 South East 87
## 1642 2020-04-21 South East 51
## 1643 2020-04-22 South East 54
## 1644 2020-04-23 South East 57
## 1645 2020-04-24 South East 64
## 1646 2020-04-25 South East 51
## 1647 2020-04-26 South East 51
## 1648 2020-04-27 South East 41
## 1649 2020-04-28 South East 40
## 1650 2020-04-29 South East 47
## 1651 2020-04-30 South East 29
## 1652 2020-05-01 South East 37
## 1653 2020-05-02 South East 36
## 1654 2020-05-03 South East 17
## 1655 2020-05-04 South East 35
## 1656 2020-05-05 South East 29
## 1657 2020-05-06 South East 25
## 1658 2020-05-07 South East 27
## 1659 2020-05-08 South East 26
## 1660 2020-05-09 South East 28
## 1661 2020-05-10 South East 19
## 1662 2020-05-11 South East 25
## 1663 2020-05-12 South East 27
## 1664 2020-05-13 South East 18
## 1665 2020-05-14 South East 32
## 1666 2020-05-15 South East 25
## 1667 2020-05-16 South East 22
## 1668 2020-05-17 South East 18
## 1669 2020-05-18 South East 22
## 1670 2020-05-19 South East 12
## 1671 2020-05-20 South East 22
## 1672 2020-05-21 South East 15
## 1673 2020-05-22 South East 17
## 1674 2020-05-23 South East 21
## 1675 2020-05-24 South East 17
## 1676 2020-05-25 South East 13
## 1677 2020-05-26 South East 19
## 1678 2020-05-27 South East 19
## 1679 2020-05-28 South East 12
## 1680 2020-05-29 South East 22
## 1681 2020-05-30 South East 8
## 1682 2020-05-31 South East 12
## 1683 2020-06-01 South East 11
## 1684 2020-06-02 South East 13
## 1685 2020-06-03 South East 18
## 1686 2020-06-04 South East 11
## 1687 2020-06-05 South East 11
## 1688 2020-06-06 South East 10
## 1689 2020-06-07 South East 12
## 1690 2020-06-08 South East 8
## 1691 2020-06-09 South East 10
## 1692 2020-06-10 South East 11
## 1693 2020-06-11 South East 5
## 1694 2020-06-12 South East 6
## 1695 2020-06-13 South East 7
## 1696 2020-06-14 South East 7
## 1697 2020-06-15 South East 8
## 1698 2020-06-16 South East 14
## 1699 2020-06-17 South East 9
## 1700 2020-06-18 South East 4
## 1701 2020-06-19 South East 7
## 1702 2020-06-20 South East 5
## 1703 2020-06-21 South East 3
## 1704 2020-06-22 South East 2
## 1705 2020-06-23 South East 9
## 1706 2020-06-24 South East 7
## 1707 2020-06-25 South East 5
## 1708 2020-06-26 South East 8
## 1709 2020-06-27 South East 9
## 1710 2020-06-28 South East 6
## 1711 2020-06-29 South East 5
## 1712 2020-06-30 South East 5
## 1713 2020-07-01 South East 2
## 1714 2020-07-02 South East 8
## 1715 2020-07-03 South East 3
## 1716 2020-07-04 South East 6
## 1717 2020-07-05 South East 5
## 1718 2020-07-06 South East 4
## 1719 2020-07-07 South East 6
## 1720 2020-07-08 South East 3
## 1721 2020-07-09 South East 7
## 1722 2020-07-10 South East 3
## 1723 2020-07-11 South East 4
## 1724 2020-07-12 South East 5
## 1725 2020-07-13 South East 5
## 1726 2020-07-14 South East 5
## 1727 2020-07-15 South East 6
## 1728 2020-07-16 South East 3
## 1729 2020-07-17 South East 1
## 1730 2020-07-18 South East 5
## 1731 2020-07-19 South East 2
## 1732 2020-07-20 South East 6
## 1733 2020-07-21 South East 4
## 1734 2020-07-22 South East 2
## 1735 2020-07-23 South East 3
## 1736 2020-07-24 South East 1
## 1737 2020-07-25 South East 1
## 1738 2020-07-26 South East 3
## 1739 2020-07-27 South East 1
## 1740 2020-07-28 South East 3
## 1741 2020-07-29 South East 2
## 1742 2020-07-30 South East 3
## 1743 2020-07-31 South East 1
## 1744 2020-08-01 South East 2
## 1745 2020-08-02 South East 4
## 1746 2020-08-03 South East 0
## 1747 2020-08-04 South East 0
## 1748 2020-08-05 South East 0
## 1749 2020-08-06 South East 2
## 1750 2020-08-07 South East 0
## 1751 2020-08-08 South East 2
## 1752 2020-08-09 South East 0
## 1753 2020-08-10 South East 2
## 1754 2020-08-11 South East 1
## 1755 2020-08-12 South East 1
## 1756 2020-08-13 South East 0
## 1757 2020-08-14 South East 0
## 1758 2020-08-15 South East 2
## 1759 2020-08-16 South East 1
## 1760 2020-08-17 South East 0
## 1761 2020-08-18 South East 2
## 1762 2020-08-19 South East 1
## 1763 2020-08-20 South East 0
## 1764 2020-08-21 South East 0
## 1765 2020-08-22 South East 0
## 1766 2020-08-23 South East 1
## 1767 2020-08-24 South East 0
## 1768 2020-08-25 South East 1
## 1769 2020-08-26 South East 0
## 1770 2020-08-27 South East 1
## 1771 2020-08-28 South East 2
## 1772 2020-08-29 South East 1
## 1773 2020-08-30 South East 0
## 1774 2020-08-31 South East 2
## 1775 2020-09-01 South East 1
## 1776 2020-09-02 South East 1
## 1777 2020-09-03 South East 0
## 1778 2020-09-04 South East 1
## 1779 2020-09-05 South East 0
## 1780 2020-09-06 South East 1
## 1781 2020-09-07 South East 0
## 1782 2020-09-08 South East 0
## 1783 2020-09-09 South East 0
## 1784 2020-09-10 South East 1
## 1785 2020-09-11 South East 1
## 1786 2020-09-12 South East 0
## 1787 2020-09-13 South East 3
## 1788 2020-09-14 South East 1
## 1789 2020-09-15 South East 2
## 1790 2020-09-16 South East 2
## 1791 2020-09-17 South East 3
## 1792 2020-09-18 South East 1
## 1793 2020-09-19 South East 1
## 1794 2020-09-20 South East 0
## 1795 2020-09-21 South East 3
## 1796 2020-09-22 South East 0
## 1797 2020-09-23 South East 2
## 1798 2020-09-24 South East 1
## 1799 2020-09-25 South East 3
## 1800 2020-09-26 South East 2
## 1801 2020-09-27 South East 2
## 1802 2020-09-28 South East 6
## 1803 2020-09-29 South East 3
## 1804 2020-09-30 South East 4
## 1805 2020-10-01 South East 4
## 1806 2020-10-02 South East 2
## 1807 2020-10-03 South East 1
## 1808 2020-10-04 South East 1
## 1809 2020-10-05 South East 2
## 1810 2020-10-06 South East 1
## 1811 2020-10-07 South East 4
## 1812 2020-10-08 South East 1
## 1813 2020-10-09 South East 1
## 1814 2020-10-10 South East 3
## 1815 2020-10-11 South East 3
## 1816 2020-10-12 South East 4
## 1817 2020-10-13 South East 2
## 1818 2020-10-14 South East 2
## 1819 2020-10-15 South East 3
## 1820 2020-10-16 South East 2
## 1821 2020-10-17 South East 3
## 1822 2020-10-18 South East 4
## 1823 2020-10-19 South East 7
## 1824 2020-10-20 South East 8
## 1825 2020-10-21 South East 9
## 1826 2020-10-22 South East 5
## 1827 2020-10-23 South East 7
## 1828 2020-10-24 South East 5
## 1829 2020-10-25 South East 9
## 1830 2020-10-26 South East 13
## 1831 2020-10-27 South East 10
## 1832 2020-10-28 South East 10
## 1833 2020-10-29 South East 7
## 1834 2020-10-30 South East 6
## 1835 2020-10-31 South East 15
## 1836 2020-11-01 South East 18
## 1837 2020-11-02 South East 13
## 1838 2020-11-03 South East 16
## 1839 2020-11-04 South East 10
## 1840 2020-11-05 South East 10
## 1841 2020-11-06 South East 16
## 1842 2020-11-07 South East 17
## 1843 2020-11-08 South East 18
## 1844 2020-11-09 South East 19
## 1845 2020-11-10 South East 20
## 1846 2020-11-11 South East 19
## 1847 2020-11-12 South East 20
## 1848 2020-11-13 South East 12
## 1849 2020-11-14 South East 24
## 1850 2020-11-15 South East 25
## 1851 2020-11-16 South East 22
## 1852 2020-11-17 South East 23
## 1853 2020-11-18 South East 26
## 1854 2020-11-19 South East 21
## 1855 2020-11-20 South East 18
## 1856 2020-11-21 South East 23
## 1857 2020-11-22 South East 30
## 1858 2020-11-23 South East 28
## 1859 2020-11-24 South East 26
## 1860 2020-11-25 South East 42
## 1861 2020-11-26 South East 30
## 1862 2020-11-27 South East 31
## 1863 2020-11-28 South East 24
## 1864 2020-11-29 South East 37
## 1865 2020-11-30 South East 22
## 1866 2020-12-01 South East 29
## 1867 2020-12-02 South East 33
## 1868 2020-12-03 South East 36
## 1869 2020-12-04 South East 40
## 1870 2020-12-05 South East 34
## 1871 2020-12-06 South East 32
## 1872 2020-12-07 South East 24
## 1873 2020-12-08 South East 43
## 1874 2020-12-09 South East 44
## 1875 2020-12-10 South East 37
## 1876 2020-12-11 South East 48
## 1877 2020-12-12 South East 38
## 1878 2020-12-13 South East 39
## 1879 2020-12-14 South East 38
## 1880 2020-12-15 South East 50
## 1881 2020-12-16 South East 44
## 1882 2020-12-17 South East 50
## 1883 2020-12-18 South East 44
## 1884 2020-12-19 South East 40
## 1885 2020-12-20 South East 53
## 1886 2020-12-21 South East 65
## 1887 2020-12-22 South East 57
## 1888 2020-12-23 South East 66
## 1889 2020-12-24 South East 48
## 1890 2020-12-25 South East 65
## 1891 2020-12-26 South East 69
## 1892 2020-12-27 South East 70
## 1893 2020-12-28 South East 77
## 1894 2020-12-29 South East 73
## 1895 2020-12-30 South East 78
## 1896 2020-12-31 South East 78
## 1897 2021-01-01 South East 53
## 1898 2021-01-02 South East 84
## 1899 2021-01-03 South East 74
## 1900 2021-01-04 South East 88
## 1901 2021-01-05 South East 94
## 1902 2021-01-06 South East 111
## 1903 2021-01-07 South East 100
## 1904 2021-01-08 South East 106
## 1905 2021-01-09 South East 89
## 1906 2021-01-10 South East 95
## 1907 2021-01-11 South East 84
## 1908 2021-01-12 South East 34
## 1909 2020-03-01 South West 0
## 1910 2020-03-02 South West 0
## 1911 2020-03-03 South West 0
## 1912 2020-03-04 South West 0
## 1913 2020-03-05 South West 0
## 1914 2020-03-06 South West 0
## 1915 2020-03-07 South West 0
## 1916 2020-03-08 South West 0
## 1917 2020-03-09 South West 0
## 1918 2020-03-10 South West 0
## 1919 2020-03-11 South West 1
## 1920 2020-03-12 South West 0
## 1921 2020-03-13 South West 0
## 1922 2020-03-14 South West 1
## 1923 2020-03-15 South West 0
## 1924 2020-03-16 South West 0
## 1925 2020-03-17 South West 2
## 1926 2020-03-18 South West 2
## 1927 2020-03-19 South West 4
## 1928 2020-03-20 South West 3
## 1929 2020-03-21 South West 6
## 1930 2020-03-22 South West 7
## 1931 2020-03-23 South West 8
## 1932 2020-03-24 South West 7
## 1933 2020-03-25 South West 9
## 1934 2020-03-26 South West 11
## 1935 2020-03-27 South West 13
## 1936 2020-03-28 South West 21
## 1937 2020-03-29 South West 18
## 1938 2020-03-30 South West 23
## 1939 2020-03-31 South West 23
## 1940 2020-04-01 South West 21
## 1941 2020-04-02 South West 23
## 1942 2020-04-03 South West 30
## 1943 2020-04-04 South West 42
## 1944 2020-04-05 South West 32
## 1945 2020-04-06 South West 34
## 1946 2020-04-07 South West 39
## 1947 2020-04-08 South West 47
## 1948 2020-04-09 South West 24
## 1949 2020-04-10 South West 46
## 1950 2020-04-11 South West 43
## 1951 2020-04-12 South West 23
## 1952 2020-04-13 South West 27
## 1953 2020-04-14 South West 24
## 1954 2020-04-15 South West 32
## 1955 2020-04-16 South West 29
## 1956 2020-04-17 South West 33
## 1957 2020-04-18 South West 25
## 1958 2020-04-19 South West 31
## 1959 2020-04-20 South West 26
## 1960 2020-04-21 South West 26
## 1961 2020-04-22 South West 23
## 1962 2020-04-23 South West 17
## 1963 2020-04-24 South West 19
## 1964 2020-04-25 South West 15
## 1965 2020-04-26 South West 27
## 1966 2020-04-27 South West 13
## 1967 2020-04-28 South West 17
## 1968 2020-04-29 South West 15
## 1969 2020-04-30 South West 26
## 1970 2020-05-01 South West 6
## 1971 2020-05-02 South West 7
## 1972 2020-05-03 South West 10
## 1973 2020-05-04 South West 17
## 1974 2020-05-05 South West 14
## 1975 2020-05-06 South West 19
## 1976 2020-05-07 South West 16
## 1977 2020-05-08 South West 6
## 1978 2020-05-09 South West 11
## 1979 2020-05-10 South West 5
## 1980 2020-05-11 South West 8
## 1981 2020-05-12 South West 7
## 1982 2020-05-13 South West 7
## 1983 2020-05-14 South West 6
## 1984 2020-05-15 South West 4
## 1985 2020-05-16 South West 4
## 1986 2020-05-17 South West 6
## 1987 2020-05-18 South West 4
## 1988 2020-05-19 South West 6
## 1989 2020-05-20 South West 1
## 1990 2020-05-21 South West 9
## 1991 2020-05-22 South West 7
## 1992 2020-05-23 South West 6
## 1993 2020-05-24 South West 3
## 1994 2020-05-25 South West 8
## 1995 2020-05-26 South West 11
## 1996 2020-05-27 South West 5
## 1997 2020-05-28 South West 10
## 1998 2020-05-29 South West 7
## 1999 2020-05-30 South West 3
## 2000 2020-05-31 South West 2
## 2001 2020-06-01 South West 7
## 2002 2020-06-02 South West 2
## 2003 2020-06-03 South West 7
## 2004 2020-06-04 South West 2
## 2005 2020-06-05 South West 2
## 2006 2020-06-06 South West 1
## 2007 2020-06-07 South West 3
## 2008 2020-06-08 South West 3
## 2009 2020-06-09 South West 0
## 2010 2020-06-10 South West 1
## 2011 2020-06-11 South West 2
## 2012 2020-06-12 South West 2
## 2013 2020-06-13 South West 2
## 2014 2020-06-14 South West 0
## 2015 2020-06-15 South West 2
## 2016 2020-06-16 South West 2
## 2017 2020-06-17 South West 0
## 2018 2020-06-18 South West 0
## 2019 2020-06-19 South West 0
## 2020 2020-06-20 South West 2
## 2021 2020-06-21 South West 0
## 2022 2020-06-22 South West 1
## 2023 2020-06-23 South West 1
## 2024 2020-06-24 South West 1
## 2025 2020-06-25 South West 0
## 2026 2020-06-26 South West 3
## 2027 2020-06-27 South West 0
## 2028 2020-06-28 South West 0
## 2029 2020-06-29 South West 1
## 2030 2020-06-30 South West 0
## 2031 2020-07-01 South West 0
## 2032 2020-07-02 South West 0
## 2033 2020-07-03 South West 0
## 2034 2020-07-04 South West 0
## 2035 2020-07-05 South West 1
## 2036 2020-07-06 South West 0
## 2037 2020-07-07 South West 0
## 2038 2020-07-08 South West 2
## 2039 2020-07-09 South West 0
## 2040 2020-07-10 South West 1
## 2041 2020-07-11 South West 0
## 2042 2020-07-12 South West 0
## 2043 2020-07-13 South West 1
## 2044 2020-07-14 South West 0
## 2045 2020-07-15 South West 0
## 2046 2020-07-16 South West 0
## 2047 2020-07-17 South West 1
## 2048 2020-07-18 South West 0
## 2049 2020-07-19 South West 0
## 2050 2020-07-20 South West 0
## 2051 2020-07-21 South West 0
## 2052 2020-07-22 South West 0
## 2053 2020-07-23 South West 0
## 2054 2020-07-24 South West 0
## 2055 2020-07-25 South West 0
## 2056 2020-07-26 South West 0
## 2057 2020-07-27 South West 0
## 2058 2020-07-28 South West 0
## 2059 2020-07-29 South West 0
## 2060 2020-07-30 South West 1
## 2061 2020-07-31 South West 0
## 2062 2020-08-01 South West 0
## 2063 2020-08-02 South West 0
## 2064 2020-08-03 South West 0
## 2065 2020-08-04 South West 0
## 2066 2020-08-05 South West 0
## 2067 2020-08-06 South West 0
## 2068 2020-08-07 South West 0
## 2069 2020-08-08 South West 0
## 2070 2020-08-09 South West 0
## 2071 2020-08-10 South West 0
## 2072 2020-08-11 South West 0
## 2073 2020-08-12 South West 0
## 2074 2020-08-13 South West 0
## 2075 2020-08-14 South West 1
## 2076 2020-08-15 South West 0
## 2077 2020-08-16 South West 0
## 2078 2020-08-17 South West 2
## 2079 2020-08-18 South West 0
## 2080 2020-08-19 South West 0
## 2081 2020-08-20 South West 0
## 2082 2020-08-21 South West 0
## 2083 2020-08-22 South West 0
## 2084 2020-08-23 South West 0
## 2085 2020-08-24 South West 0
## 2086 2020-08-25 South West 1
## 2087 2020-08-26 South West 0
## 2088 2020-08-27 South West 1
## 2089 2020-08-28 South West 0
## 2090 2020-08-29 South West 0
## 2091 2020-08-30 South West 0
## 2092 2020-08-31 South West 0
## 2093 2020-09-01 South West 0
## 2094 2020-09-02 South West 0
## 2095 2020-09-03 South West 0
## 2096 2020-09-04 South West 0
## 2097 2020-09-05 South West 0
## 2098 2020-09-06 South West 0
## 2099 2020-09-07 South West 0
## 2100 2020-09-08 South West 1
## 2101 2020-09-09 South West 0
## 2102 2020-09-10 South West 0
## 2103 2020-09-11 South West 0
## 2104 2020-09-12 South West 0
## 2105 2020-09-13 South West 1
## 2106 2020-09-14 South West 0
## 2107 2020-09-15 South West 0
## 2108 2020-09-16 South West 0
## 2109 2020-09-17 South West 1
## 2110 2020-09-18 South West 0
## 2111 2020-09-19 South West 0
## 2112 2020-09-20 South West 1
## 2113 2020-09-21 South West 0
## 2114 2020-09-22 South West 0
## 2115 2020-09-23 South West 0
## 2116 2020-09-24 South West 1
## 2117 2020-09-25 South West 0
## 2118 2020-09-26 South West 0
## 2119 2020-09-27 South West 0
## 2120 2020-09-28 South West 0
## 2121 2020-09-29 South West 0
## 2122 2020-09-30 South West 0
## 2123 2020-10-01 South West 0
## 2124 2020-10-02 South West 1
## 2125 2020-10-03 South West 0
## 2126 2020-10-04 South West 0
## 2127 2020-10-05 South West 0
## 2128 2020-10-06 South West 1
## 2129 2020-10-07 South West 0
## 2130 2020-10-08 South West 1
## 2131 2020-10-09 South West 1
## 2132 2020-10-10 South West 0
## 2133 2020-10-11 South West 4
## 2134 2020-10-12 South West 2
## 2135 2020-10-13 South West 0
## 2136 2020-10-14 South West 3
## 2137 2020-10-15 South West 1
## 2138 2020-10-16 South West 2
## 2139 2020-10-17 South West 8
## 2140 2020-10-18 South West 2
## 2141 2020-10-19 South West 2
## 2142 2020-10-20 South West 3
## 2143 2020-10-21 South West 6
## 2144 2020-10-22 South West 6
## 2145 2020-10-23 South West 5
## 2146 2020-10-24 South West 5
## 2147 2020-10-25 South West 5
## 2148 2020-10-26 South West 7
## 2149 2020-10-27 South West 6
## 2150 2020-10-28 South West 8
## 2151 2020-10-29 South West 11
## 2152 2020-10-30 South West 8
## 2153 2020-10-31 South West 4
## 2154 2020-11-01 South West 5
## 2155 2020-11-02 South West 11
## 2156 2020-11-03 South West 7
## 2157 2020-11-04 South West 8
## 2158 2020-11-05 South West 5
## 2159 2020-11-06 South West 11
## 2160 2020-11-07 South West 10
## 2161 2020-11-08 South West 10
## 2162 2020-11-09 South West 12
## 2163 2020-11-10 South West 6
## 2164 2020-11-11 South West 13
## 2165 2020-11-12 South West 17
## 2166 2020-11-13 South West 9
## 2167 2020-11-14 South West 8
## 2168 2020-11-15 South West 16
## 2169 2020-11-16 South West 18
## 2170 2020-11-17 South West 17
## 2171 2020-11-18 South West 26
## 2172 2020-11-19 South West 15
## 2173 2020-11-20 South West 25
## 2174 2020-11-21 South West 25
## 2175 2020-11-22 South West 23
## 2176 2020-11-23 South West 14
## 2177 2020-11-24 South West 20
## 2178 2020-11-25 South West 25
## 2179 2020-11-26 South West 16
## 2180 2020-11-27 South West 21
## 2181 2020-11-28 South West 35
## 2182 2020-11-29 South West 15
## 2183 2020-11-30 South West 21
## 2184 2020-12-01 South West 18
## 2185 2020-12-02 South West 15
## 2186 2020-12-03 South West 14
## 2187 2020-12-04 South West 20
## 2188 2020-12-05 South West 17
## 2189 2020-12-06 South West 13
## 2190 2020-12-07 South West 14
## 2191 2020-12-08 South West 18
## 2192 2020-12-09 South West 21
## 2193 2020-12-10 South West 20
## 2194 2020-12-11 South West 20
## 2195 2020-12-12 South West 15
## 2196 2020-12-13 South West 19
## 2197 2020-12-14 South West 20
## 2198 2020-12-15 South West 18
## 2199 2020-12-16 South West 9
## 2200 2020-12-17 South West 24
## 2201 2020-12-18 South West 10
## 2202 2020-12-19 South West 21
## 2203 2020-12-20 South West 19
## 2204 2020-12-21 South West 20
## 2205 2020-12-22 South West 10
## 2206 2020-12-23 South West 16
## 2207 2020-12-24 South West 18
## 2208 2020-12-25 South West 19
## 2209 2020-12-26 South West 24
## 2210 2020-12-27 South West 24
## 2211 2020-12-28 South West 20
## 2212 2020-12-29 South West 20
## 2213 2020-12-30 South West 14
## 2214 2020-12-31 South West 25
## 2215 2021-01-01 South West 27
## 2216 2021-01-02 South West 22
## 2217 2021-01-03 South West 24
## 2218 2021-01-04 South West 30
## 2219 2021-01-05 South West 30
## 2220 2021-01-06 South West 24
## 2221 2021-01-07 South West 29
## 2222 2021-01-08 South West 32
## 2223 2021-01-09 South West 23
## 2224 2021-01-10 South West 25
## 2225 2021-01-11 South West 23
## 2226 2021-01-12 South West 12We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Wednesday 13 Jan 2021.
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_reportWe plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lagThis analysis suggests that the best lag is 16 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 16 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -15.399 -11.634 -4.423 7.870 19.323
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.631e+00 6.428e-02 72.04 <2e-16 ***
## note_lag 1.600e-05 9.154e-07 17.48 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 103.6375)
##
## Null deviance: 51143 on 263 degrees of freedom
## Residual deviance: 28532 on 262 degrees of freedom
## (16 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 102.595763 1.000016
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 90.242723 116.112854
## note_lag 1.000014 1.000018
Rsq(lag_mod)
## [1] 0.4421053
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fitThis is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom")
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_regWe save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))The following information documents the system on which the document was compiled.
This provides information on the operating system.
Sys.info()
## sysname
## "Darwin"
## release
## "19.6.0"
## version
## "Darwin Kernel Version 19.6.0: Thu Oct 29 22:56:45 PDT 2020; root:xnu-6153.141.2.2~1/RELEASE_X86_64"
## nodename
## "Mac-1610620818370.local"
## machine
## "x86_64"
## login
## "root"
## user
## "runner"
## effective_user
## "runner"This provides information on the version of R used:
This provides information on the packages used:
sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.5 ggpubr_0.4.0 lubridate_1.7.9.2
## [4] chngpt_2020.10-12 cyphr_1.1.0 DT_0.17
## [7] kableExtra_1.3.1 janitor_2.1.0 remotes_2.2.0
## [10] projections_0.5.2 earlyR_0.0.5 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.3 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.6 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
## [25] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.3
## [28] tidyverse_1.3.0 here_1.0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] minqa_1.2.4 colorspace_2.0-0 selectr_0.4-2 ggsignif_0.6.0
## [5] ellipsis_0.3.1 rprojroot_2.0.2 snakecase_0.11.0 fs_1.5.0
## [9] rstudioapi_0.13 farver_2.0.3 fansi_0.4.1 splines_4.0.3
## [13] knitr_1.30 jsonlite_1.7.2 nloptr_1.2.2.2 broom_0.7.3
## [17] dbplyr_2.0.0 compiler_4.0.3 httr_1.4.2 backports_1.2.1
## [21] assertthat_0.2.1 Matrix_1.2-18 cli_2.2.0 htmltools_0.5.1
## [25] tools_4.0.3 gtable_0.3.0 glue_1.4.2 Rcpp_1.0.5
## [29] carData_3.0-4 cellranger_1.1.0 vctrs_0.3.6 nlme_3.1-149
## [33] matchmaker_0.1.1 crosstalk_1.1.1 xfun_0.20 ps_1.5.0
## [37] openxlsx_4.2.3 lme4_1.1-26 lifecycle_0.2.0 statmod_1.4.35
## [41] rstatix_0.6.0 MASS_7.3-53 scales_1.1.1 hms_1.0.0
## [45] parallel_4.0.3 sodium_1.1 yaml_2.2.1 curl_4.3
## [49] gridExtra_2.3 stringi_1.5.3 kyotil_2020.10-12 boot_1.3-25
## [53] zip_2.1.1 rlang_0.4.10 pkgconfig_2.0.3 evaluate_0.14
## [57] lattice_0.20-41 labeling_0.4.2 htmlwidgets_1.5.3 cowplot_1.1.1
## [61] tidyselect_1.1.0 plyr_1.8.6 magrittr_2.0.1 R6_2.5.0
## [65] generics_0.1.0 DBI_1.1.0 pillar_1.4.7 haven_2.3.1
## [69] foreign_0.8-80 withr_2.3.0 mgcv_1.8-33 survival_3.2-7
## [73] abind_1.4-5 modelr_0.1.8 crayon_1.3.4 car_3.0-10
## [77] utf8_1.1.4 rmarkdown_2.6 viridis_0.5.1 grid_4.0.3
## [81] readxl_1.3.1 data.table_1.13.6 reprex_0.3.0 digest_0.6.27
## [85] webshot_0.5.2 munsell_0.5.0 viridisLite_0.3.0